This invention relates in general to data compression and particularly to digital data compression. Specifically, the invention relates to a method and apparatus that provides improved wavelet filter operation for digital data systems.
With the advent of technologies and services related to teleconferencing and digital image storage, considerable progress has been made in the field of digital signal processing. As will be appreciated by those skilled in the art, digital signal processing typically relates to systems, devices, and methodologies for generating a sampled data signal, compressing the signal for storage and/or transmission, and thereafter reconstructing the original data from the compressed signal. Critical to any highly efficient, cost effective digital signal processing system is the methodology used for achieving compression.
As is known in the art, data compression refers to the steps performed to map an original data signal into a bit stream suitable for communication over a channel or storage in a suitable medium. Methodologies capable of minimizing the amount of information necessary to represent and recover an original data are desirable in order to lower computational complexity and cost. In addition to cost, simplicity of hardware and software implementations capable of providing high quality data reproduction with minimal delay are likewise desirable.
To present, the next standard of JPEG 2000 (Joint Photographic Experts Group) systems for still images proposes algorithms which use a wavelet to achieve decomposition of an input signal. In systems utilizing a wavelet, the data is typically divided through low pass and high pass filters. The implementation of wavelet filters is typically achieved using a filter bank, and in many instances involves several levels of filtering. Filter bank operation is often implemented through numerous multiplications and additions between the wavelet coefficients and the input data. The process of implementing filter banks can be extremely time consuming unless very fast multipliers are used. Such multipliers typically utilize parallel processing which requires the use of fast clocks with higher current consumption as well as requiring larger chip area in their implementation.
Some prior art filter banks have eliminated multipliers through the use canonical signed digit(s) (CSD). CSD is a powers of two representation of an integer. (e.g. 9=23+20). One such filter bank is described in an IEEE article entitled xe2x80x9cThe Design of Low Complexity Linear-Phase FIR Filter Banks Using Powers-of-Two Coefficients with an Application to Subband Image Codingxe2x80x9d Vol. 1, No. 4, December 1991. This prior art system develops a constraint equation based on an imposed pure-delay requirement implied by perfect reconstruction. To overcome the non-linearity in this equation, low pass filter coefficients are first restricted to CSD without any constraint, then a suitable set of high pass CSD coefficients are obtained by using a complex optimization algorithm such that the constraint equation is met. This prior art system, however, is restricted by a two-channel linear phase FIR filter bank.
While certain wavelet transforms are closely linked to the two band (or two-channel) perfect reconstruction (i.e. lossless) filter bank, this is not always the case. Wavelet based CODECs are often used in both lossless (perfect reconstruction) and lossy (involving quantization) digital data systems. Quantization refers to the technique of taking a bit stream of data and compressing it for later reproduction. In most data compression applications, the presence of quantization in a lossy system implies a rate-distortion performance which usually bounds the quality of the recovered image. Thus, a pure-delay requirement on the design and representation of the wavelet coefficients is too restrictive, often resulting in implementations that are more complex than required.
There is a need for a method and apparatus which provides improved wavelet filtering for both lossy and lossless digital data systems. Such wavelets should be implemented so as to minimize current drain and reduce computational intensity.